Artificial Intelligence and Business Strategy
In collaboration withBCG
What to Read Next
Technology now touches and transforms every aspect of personal productivity in the workplace. Mobile devices, bots, and digital assistants are ubiquitous, while managers increasingly use key performance indicator (KPI) dashboards to monitor and measure employee performance. In industry after global industry, effectively collaborating with technology is as important as effectively collaborating with people.
Continually boosting the value of employees in this environment — especially knowledge workers — poses a difficult design challenge. Designing and training smarter algorithms may be cheaper and easier than retraining smart people. Advocates of autonomous systems and machine learning typically innovate to minimize or marginalize human involvement in business processes. For them, people are part of the problem, not the solution.
Organizations that take productivity seriously, however, understand that false dichotomies make poor investments: Smarter machines can — and should — be keys to unlocking greater returns from human capital.
My latest research suggests a novel and perhaps counterintuitive approach to the future of personal productivity. This approach, influenced more by behavioral economics insights than algorithmic innovation, challenges popular, data-driven digital paradigms.
The premise is that digital technology can drive greater self-awareness and self-assessment about how individuals create and contribute to enterprise value. The design focus shifts from digital assistants to digital assistance. Think of an AI that stands for “Augmented Introspection” as well as “Artificial Intelligence.”
The Workforce Driver: A Need for Higher-Performance Versions of Employees
As workforces confront more agile and adaptive global competition, traditional competencies and typical or ordinary personal performance growth may no longer suffice. That’s one reason digital innovators have focused on automating people out of processes or giving them “smarter tools” to better perform their tasks.
The rise of apps, bots, software agents, and digital assistants — such as Amazon’s Alexa, Apple’s Siri, and Microsoft’s Cortana — has been remarkable. Acoustic agents and textual chatbots that respond to human requests are becoming integral to human effectiveness both at home and at work. Nevertheless, their increasing intelligence and ingenuity should not be allowed to define or dominate personal productivity debates. Yes, the prevailing agent/bot paradigm offers up smarter and better digital actors to do one’s bidding, but that bidding is done for the same old self. What’s the better human capital investment: Surrounding the same old self with smarter agents and better bots, or empowering individuals with data and technology to craft high-performance versions of themselves?
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The process is simple. Identify the strengths/attributes we need to boost and the weaknesses/aspects we need to mitigate given organizational roles and responsibilities. In other words, use technology to craft a “better self” rather than build a “better agent.” For example:
- A global project manager wants to encourage greater cooperation, collaboration, and esprit within her team. Her customized, self-analysis software performs a social-network analysis, prioritizes project milestones, and reviews post-meeting communications to propose a daily improvement checklist.
- An executive recognizes his written communications lack clarity, energy, and forcefulness. He shares his missives and messages with software such as IBM’s Watson Tone Analyzer, which proposes revisions that bring force and focus to the prose.
- A technically competent but creatively uninspired user-interface (UX) designer uses a specially designed visual recommendation engine to produce bolder UX design.
In each case, individuals can choose customized software, digital tools, and user-specific options to improve performance and productivity by addressing key attributes — be they affective qualities like boldness or friendliness, or technical skills such as facilitation and formulating testable hypotheses. As with Amazon, Google Maps, and Netflix, people receive actionable, data-driven recommendations informed by algorithms explicitly designed to create a desired self. Please note: The technologies to do this effectively exist.
As we draw — and build — upon abilities both innate and external, we are developing multiple selves, digital versions of the self with one or more personal dimensions deliberately designed to significantly outperform and generate greater economic impact and organizational influence than the ordinary or average self. Ongoing innovation assures that people will be better able to identify, manage, and measurably improve their best selves. Instead of recommendation engines for books to read or movies to watch, “multiple selfers” will access actionable insights and advice on what to say, when to speak up, with whom to work, and how best to behave via personal dashboards.
Optimizing the capabilities of a digital self is thus an opportunity to empower, not replace, the human self. While today’s digital agents perform tasks to deliver desired outcomes, digital selves go further — they define those tasks and outcomes. Consequently, the true technical challenge lies less with designing better agents than with enabling people to build more productive and more valuable versions of themselves.
Building on Social Science Research
The social science research and concepts supporting the multiple-self paradigm is impressively multidisciplinary and rich. For example, Daniel Kahneman’s Nobel Prize-winning work defining cognitive biases offers clear frameworks for designing digital selves. The work of the late Marvin Minsky, including his Society of Mind (Simon & Schuster, 1988), also offers a veritable road map into what modules of the mind are best positioned for digital augmentation and enhancement. These works strongly suggest that cultivating and managing multiple selves will exponentially improve personal productivity. To be sure, that doesn’t make software agents less valuable, but it persuasively argues that the productive value of human agency is underappreciated.
In addition, widespread adoption of “quantified self” tools and technologies — think wearable devices and sensors — promise ever-richer datasets for multiple-selves design. Technologies that track steps and heart rates already draw actionable inferences about individual energy levels and mood. Jawbone, Fitbit, and other health-tracking mobile device apps can easily play significant workplace roles in assessing mental acuity and attention, just as they now do for physical fitness. The workday is near when monitors and personal KPI dashboards will physiologically sense when users are not in the mood to take advice. Individuals and their organizations will be able to track which “selves” deliver the best performance and outcomes.
In the long term, more granular self data and analytics will prove essential ingredients for boosting personal productivity and performance inside the enterprise and out. The business case is simple and straightforward: Well-managed multiple selves will reliably outperform and out-produce average selves assisted by agents and bots.
Born-digital companies and cultures like Google, Facebook, Amazon, and Netflix, with their digital sophistication and algorithmic chops, are supremely well positioned to lead the way and gain further competitive advantage with workforces of high-performance multiple selves. It’s not science fiction to empirically argue that emergent digital enterprise tools, techniques, and analytics will collectively create healthy and self-sustaining digital ecosystems for multiple-selves design.
“Know thyself” is one of the oldest and wisest of classic aphorisms. The rise of ever smarter, ever more disruptive technologies, however, demands a 21st-century digital update: Know thy selves. Our research aspires to help productive employees and enterprises alike to know themselves, and their digital potential, better than ever.